Liver Disease Prediction Using Ensemble Technique

Tanuku, Sai Rohith; Kumar, Addike Ajay; Somaraju, Sai Roop; Dattuluri, Rushitaa; Reddy, Madana Vamshi Krishna; Jain, S. K.

doi:10.1109/icaccs54159.2022.9784999

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…The ensemble model combines multiple models to reduce the model error and maintain the model’s generalisation. Bagging, boosting, stacking, and voting are the four major classes of ensemble learning algorithms, and it is critical to understand each one and incorporate them into every predictive modelling endeavour [ 9 ].…”

Section: Ensemble Learningmentioning

confidence: 99%

Ensemble Learning for Disease Prediction: A Review

et al. 2023

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Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016–2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.

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Section: Ensemble Learningmentioning

confidence: 99%

Ensemble Learning for Disease Prediction: A Review

et al. 2023

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Survey on Liver Disease Prediction and Recommendation

Sai,

Geethika,

Peddi

et al. 2023

2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT)

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A Machine Learning‐Based Framework for Accurate and Early Diagnosis of Liver Diseases: A Comprehensive Study on Feature Selection, Data Imbalance, and Algorithmic Performance

Rehman,

Butt,

Ali

et al. 2024

International Journal of Intelligent Systems

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The liver is the largest organ of the human body with more than 500 vital functions. In recent decades, a large number of liver patients have been reported with diseases such as cirrhosis, fibrosis, or other liver disorders. There is a need for effective, early, and accurate identification of individuals suffering from such disease so that the person may recover before the disease spreads and becomes fatal. For this, applications of machine learning are playing a significant role. Despite the advancements, existing systems remain inconsistent in performance due to limited feature selection and data imbalance. In this article, we reviewed 58 articles extracted from 5 different electronic repositories published from January 2015 to 2023. After a systematic and protocol‐based review, we answered 6 research questions about machine learning algorithms. The identification of effective feature selection techniques, data imbalance management techniques, accurate machine learning algorithms, a list of available data sets with their URLs and characteristics, and feature importance based on usage has been identified for diagnosing liver disease. The reason to select this research question is, in any machine learning framework, the role of dimensionality reduction, data imbalance management, machine learning algorithm with its accuracy, and data itself is very significant. Based on the conducted review, a framework, machine learning‐based liver disease diagnosis (MaLLiDD), has been proposed and validated using three datasets. The proposed framework classified liver disorders with 99.56%, 76.56%, and 76.11% accuracy. In conclusion, this article addressed six research questions by identifying effective feature selection techniques, data imbalance management techniques, algorithms, datasets, and feature importance based on usage. It also demonstrated a high accuracy with the framework for early diagnosis, marking a significant advancement.

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Liver Disease Prediction Using Ensemble Technique

Cited by 4 publications

References 11 publications

Ensemble Learning for Disease Prediction: A Review

Ensemble Learning for Disease Prediction: A Review

Survey on Liver Disease Prediction and Recommendation

A Machine Learning‐Based Framework for Accurate and Early Diagnosis of Liver Diseases: A Comprehensive Study on Feature Selection, Data Imbalance, and Algorithmic Performance

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